Multi-Modal Embeddings for Real-Time User Intent Prediction
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Solution Overview
Problem
Computing devices struggle to accurately determine user intent due to linguistic complexities such as colloquialisms, homonyms, local dialects, and slang, leading to confusion in semantic analysis and incorrect correlations between synonymous terms.
Innovation Solution
Employing multi-modal embeddings using time-sequenced historic user data, generated through modal-specific encoders and a multi-modal embeddings generator, to map user interactions into a shared vector space, enabling accurate real-time user intent prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional semantic analysis is used to process user input, then the system can handle basic text processing, but it fails to accurately determine user intent due to linguistic complexities such as colloquialisms, homonyms, polysemes, local dialects, and slang
Solution Approach 1:
The patent segments user interactions into multiple modalities (text, audio, video, transactional data) and processes each modality separately through dedicated encoders before integrating them. This segmentation allows the system to handle linguistic complexities in each modality specifically, improving intent prediction accuracy without overwhelming the system with undifferentiated complex data
Solution Approach 2:
The patent transforms user interaction data from traditional text-only analysis into multi-dimensional embeddings that incorporate temporal sequences and multiple modalities. By mapping interactions into a shared vector space with time-sequenced embeddings, the system adds dimensional depth to understand contextual meaning, resolving ambiguities from colloquialisms and homonyms through enriched feature representation
2Adaptability or versatility
If computing devices use traditional text analysis methods, then processing is simpler, but they cannot correlate synonymous interrelated subjects or understand user's distinctive linguistic styles
Solution Approach 1:
The patent performs preliminary encoding of user interactions into multi-modal embeddings before intent prediction. By pre-processing text, audio, video, and transactional data into standardized vector representations with temporal sequencing, the system prepares enriched contextual information in advance, ensuring no user-specific linguistic patterns are lost during subsequent analysis
Solution Approach 2:
The patent creates a universal shared vector space that integrates multiple modalities (text, audio, video, transactional data) into a common representation framework. This multi-functional embedding space allows the system to adapt to various linguistic styles and correlate synonymous subjects across different interaction types, maintaining comprehensive user context while enabling versatile analysis
3Measurement precision
If the system processes user interactions in real-time without historical context, then response speed is faster, but intent prediction accuracy decreases
Solution Approach 1:
The patent implements periodic updates of multi-modal embeddings as new user interactions occur. Instead of reprocessing all historical data continuously, the system periodically incorporates new interactions into the existing embedding framework, maintaining accurate intent prediction while limiting computational overhead and processing time
Solution Approach 2:
The patent creates dynamic, time-sequenced embeddings that evolve as users interact with the system. The embeddings adapt in real-time to changing user intent while maintaining historical context through sequential mapping, allowing the system to balance processing speed with accurate intent prediction by updating representations dynamically rather than statically
Data Source
AI summary
Aspects described herein may improve predictions of real-time user intent by generating multi-modal embeddings based on previous user interactions. The previous user interaction data may be received from multiple different modalities. The interaction data may be analyzed and labeled with user intent for each previous user interaction, then sequentially organized. The multi-modal embeddings may be generated by modal-specific encoders and a multi-modal embeddings generator. A trained machine learning model may use, as input, the multi-modal embeddings and personal data to determine real-time user intent at the beginning of an interaction. The multi-modal embeddings may act as a set of unique and specific output vectors that allow a computing device to recognize and correlate synonymous terms and interrelated subjects, usually a difficult task for computing devices, thereby improving the technological process of analyzing, in real-time, user intent.